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\newcommand{\our}{Paper2Video}
\newcommand{\bench}{Paper2Video}
% \newcommand{\agent}{PaperShow}
\newcommand{\agent}{PaperTalker}
% Note. For the workshop paper template, both \title{} and \workshoptitle{} are required, with the former indicating the paper title shown in the title and the latter indicating the workshop title displayed in the footnote. 
\title{\includegraphics[width=0.7cm, height=0.7cm]{figure/logo_.png} Paper2Video:
Automatic Video Generation from Scientific Papers}

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\author{
  Zeyu Zhu\textsuperscript{*}, 
  Kevin Qinghong Lin\textsuperscript{*}, 
  Mike Zheng Shou\textsuperscript{\Letter}\\
  \\
  Show Lab, National University of Singapore \\
}

\iclrfinalcopy
\begin{document}


\maketitle
\let\thefootnote\relax
\footnotetext{$^*$ Equal contribution.}
\let\thefootnote\relax
\footnotetext{$^{\textrm{\Letter}}$ Corresponding author.}
\vspace{-2\baselineskip} 
\begin{abstract}
\vspace{-1\baselineskip} 
Academic presentation videos have become an essential medium for research communication, yet producing them remains highly labor-intensive, often requiring hours of slide design, recording, and editing for a short 2 to 10 minutes video. 
Unlike natural video, presentation video generation involves distinctive challenges: long-context inputs from research papers, dense multi-modal information (text, figures, tables), and the need to coordinate multiple aligned channels such as slides, subtitles, speech, and human talker. 
To address these challenges, we introduce \textbf{\bench}, the first benchmark of 101 research papers paired with author-created presentation videos, slides, and speaker metadata. We further design four tailored evaluation metrics—Meta Similarity, PresentArena, \textit{PresentQuiz}, and \textit{IP Memory}—to measure how videos convey the paper's information to the audience. Building on this foundation, we propose \textbf{\agent}, the first multi-agent framework for academic presentation video generation. It integrates slide generation with effective layout refinement by a novel effective \textit{Tree Search Visual Choice}, cursor grounding, subtitling, speech synthesis, and talking-head rendering, while parallelizing slide-wise generation for efficiency. Experiments on \bench~demonstrate that the presentation videos produced by our approach are more faithful and informative than existing baselines, establishing a practical step toward automated and ready-to-use academic video generation.
% Our datasets, agent, and codes will be \textit{fully open-sourced} to power the community.
\end{abstract}

\input{section/introduction.tex}
\input{section/related_work.tex}
\input{section/benchmark.tex}
\input{section/method.tex}
\input{section/experiment}
\input{section/conclusion}


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\input{section/appendix}



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% \section{Technical Appendices and Supplementary Material}
% Technical appendices with additional results, figures, graphs and proofs may be submitted with the paper submission before the full submission deadline (see above), or as a separate PDF in the ZIP file below before the supplementary material deadline. There is no page limit for the technical appendices.

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\end{document}